The present invention generally relates to a system, method and computer instructions for compression of four dimensional (“4D”) data. More particularly, the present invention relates to a system, method and computer instructions for compressing 4D data, wherein the data includes a first three dimensional volume and a second three dimensional volume, and wherein the data is compressed by compressing the first volume and compressing a difference between the first volume and the second volume. Further, the present invention relates to a system, method and computer instructions for compressing 4D data, wherein the data further includes a third three dimensional volume and the data is further compressed by compressing a difference between the second volume and the third volume, for example.
Technological advances in imaging have brought dynamic three dimensional (“3D”) imaging into existence. Dynamic 3D images show a 3D image over a period of time. At any point in time, the 3D image is represented by a volume. Together, all volumes over a period of time make up a sequence. Inasmuch, a sequence shows a 3D image over a period of time. Data generated by capturing a dynamic 3D image is called dynamic 3D data. Dynamic 3D data is also known as 4D data.
While all data has storage requirements, 4D data usually requires more storage than traditional data types, such as (non-dynamic) 3D data and two dimensional (“2D”) data. Storage requirements become important when data is transferred and/or stored. Data that requires more storage is a larger burden on networks when transferred. This may become a problem when networks with limited bandwidth become overburdened. Further, data that requires more storage takes up more space in memory when stored. This may become a problem when the storage space of a computing device and/or any other device with memory becomes full.
One solution to this problem is data compression. Data compression reduces the storage requirements of data. Some types of data compression preserve the characteristics of data exactly. Other types of data compression do not preserve the characteristics of data exactly. Types of data compression that do not preserve the characteristics of data exactly are often referred to as lossy compression.
The effectiveness of any data compression method usually turns on two metrics. The first metric is compression speed, or the amount of time required to compress the data. The second metric is compression efficiency, or the amount of storage space that can be saved by applying the compression method. These two metrics often have an inverse relationship. Where one compression method may be fast, it may only reduce storage requirements by a small amount. Where another compression method may be slow, it may reduce storage requirements by a greater amount.
A third metric becomes important when dealing with lossy compression. The third metric is data loss. As mentioned above, lossy compression techniques do not preserve the characteristics of data exactly. However, lossy compression techniques usually aspire to preserve data such that losses of data are not significant. When losses of data are significant, imperfections in the data (due to data lost during compression) become perceptible. Perceptible imperfections in data are often referred to as artifacts.
Methods for compressing (non-dynamic) 3D data are known. Some examples of methods for compressing (non-dynamic) 3D data are 3D wavelet compression and video compression (2D+time). Likewise, methods for compressing 4D data are known. One example is 4D wavelet compression.
However, known methods for compressing 4D data have drawbacks. For example, some 4D compression methods, such as 4D wavelet compression, are efficient (greatly reduce the storage requirements of 4D data), but take a long time to compress the data. In other words, such compression methods are computationally expensive. In another example, 3D compression methods may be applied to compress 4D data. However, while using a 3D compression method to compress a volume in a sequence may be relatively fast when compared to a 4D compression method, existing 3D compression methods that do so do not make use of redundancy in the sequence and thus have low compression efficiency. In other words, existing applications of 3D compression methods to compress 4D data do not reduce storage requirements as much as may be desired.
Thus, there is a need for a system, method and computer instructions for compression of 4D data that is both fast and efficient. Further, with regard to lossy compression, there is a need for a system, method and computer instructions for compression of 4D data that is fast, efficient and provides control over the amount of information lost during compression.